Humans tend to group together related properties in order to understand complex phenomena. When modeling large problems with limited representational resources, it is important to...
Biological networks are capable of gradual learning based on observing a large number of exemplars over time as well as of rapidly memorizing specific events as a result of a sin...
This paper examines the computational role of inhibition as it moves towards balancing concurrent excitation using the biologically-inspired Temporal Noisy-Leaky Integrator (TNLI) ...
Chris Christodoulou, Trevor G. Clarkson, John G. T...
This paper shows an application in the field of Electronic CAD of the Selfish Gene algorithm, an evolutionary algorithm based on a recent interpretation of the Darwinian theory. Te...
Fulvio Corno, Matteo Sonza Reorda, Giovanni Squill...
Many machine learning algorithms can be formulated as the minimization of a training criterion which involves (1) \training errors" on each training example and (2) some hype...